Multi-scale approach to disease spreading in social networks

City College Of New York, New York NY

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Abstract

DESCRIPTION (provided by applicant): Multi-scale approach to disease spreading in social net- works Social scientists have long identified a small number of underlying social mechanisms which individuals follow in the course of interactions with each other. In this project we propose to perform a multi-scale analysis to investigate how these micro-level social mechanisms of individual actions are transformed into network patterns at the large-scale that, in turn, determines the spread of infectious diseases in a social network. We will develop a systematic study of individual interactions in social communities, with the goal of understanding the process of establishing new network ties. This will reveal the global structure of social network as the support for the spreading of infectious diseases. This information will be later used to lower the threshold of immunization by identifying those individuals who, through their position in the network, are more likely than average to spread the disease to the larger portion of the social network. The novelty of our project lies in the multi-scale approach, where we continuously transfer information from local social mechanisms to global network properties, according to the following layers: individual actions (micro- scopic level)! global network of connections (macro-level)! disease spreading dynamics to identify the fastest spreaders of disease (superspreaders). Our approach has the novel value of studying the real-time dynamics of connections where we can directly monitor exact timing of every new tie in the network, i.e., how this dynamics affects the spread of disease. The network of connections in real communities will be analyzed in terms of state-of-the-art network theory. A large number of valuable tools from statistical network analysis will inform us about the multi-scale structural properties of the social networks and how these properties affect the transmission of disease in a network. The innovation of the proposal lies in the introduction of novel Systems Science/Statistical Physics tools to sociologically- relevant health problems. The project is a collaboration between a physicist with expertise in Complex Science (Makse) and a sociologist (Liljeros) who will provide the wide range of expertise in multi-disciplinary domains necessary to evaluate the results from scientifically varying standpoints.